Confirmation bias in structural equation modeling

In this article, a theoretical and philosophical revision of the concept of fit in structural equation modeling and its relation to a confirmation bias is developed. The neutral character of the fit indexes regarding this issue is argued, yielding the conclusion that the creation of new indexes is not a viable protection against confirmation bias. This protection relies on the model modification strategy and the behavior of scientists, rather than on mathematical or computational statistics. The differentiation between two sets of overidentifying constraints and two dimensions for nested models is introduced, leading to a general model‐modification strategy based on the framework of Popper's view of science.     

Errors of inference in structural equation modeling

Although structural equation modeling (SEM) is one of the most comprehensive and flexible approaches to data analysis currently available, it is nonetheless prone to researcher misuse and misconceptions. This article offers a brief overview of the unique capabilities of SEM and discusses common sources of user error in drawing conclusions from these analyses. We make recommendations to guide proper analytical practices and appropriate inferences and provide references for more advanced study. © 2007 Wiley Periodicals, Inc. Psychol Schs 44: 461–470, 2007.     

Issues in applied structural equation modeling research

When using the popular structural equation modeling (SEM) methodology, the issues of sample size, method of parameter estimation, assessment of model fit, and capitalization on chance are of great importance in the process of evaluating the results of an empirical study. We focus first on implications of the large‐sample theory underlying applications of the methodology. The utility for applied contexts of the asymptotically distribution‐free parameter estimation and model testing method is discussed next. We then argue for wider use of a recently developed, non conventional model‐fit assessment strategy in SEM. We conclude by discussing the issue of capitalization on chance, primarily in situations in which exploratory and confirmatory analyses are conducted on the same data set.     

A review of the mx computer program for structural equation modeling

Mx. Michael C. Neale, Virginia Institute for Psych and Behavioral Genetics, Box 980126, Richmond, VA 23298. Freeware, ftp: neale@gems.vcu.edu; http: \\www.griffin.vcu.edu\mx. Mx is available for IBM, Mac, UNIX, and VMS platforms.     

Model specification searches in structural equation modeling using tabu search

This article introduces an alternative structural equation modeling (SEM) specification search approach that is based on the Tabu search procedure. Using data with known structure, the performance of the Tabu search is illustrated. The results demonstrate the capabilities of the Tabu search procedure for conducting specification searches in SEM.     

Using the friedman method of ranks for model comparison in structural equation modeling

Empirical researchers maximize their contribution to theory development when they compare alternative theory‐inspired models under the same conditions. Yet model comparison tools in structural equation modeling—χ² difference tests, information criterion measures, and screening heuristics—have significant limitations. This article explores the use of the Friedman method of ranks as an inferential procedure for evaluating competing models. This approach has attractive properties, including limited reliance on sample size, limited distributional assumptions, an explicit multiple comparison procedure, and applicability to the comparison of nonnested models. However, this use of the Friedman method raises important issues regarding the lack of independence of observations and the power of the test.     

Using structural equation modeling to fit models incorporating principal components

The aim of this article is to consider models incorporating principal components from the perspective of structural equation modeling. These models include the principal component analysis of patterned matrices, multiple analysis of variance based on principal components, and multigroup principal component analysis. We demonstrate that these models can be fit readily using the programs LISREL 8 and Mx. The models and certain extensions are discussed, and several illustrations are given.     

Using structural equation modeling techniques to evaluate HIV risk models

Structural equation modeling (SEM) techniques were used to compare 5 methods of assessing HIV/AIDS sexual risk in a large prediction model. These were: (a) multiple measures; (b) a single latent factor; (c) modifying the computation of the dependent variables used in Methods 1 and 2 to weight sexual encounters by specific partner risk; (d) use of risk composites, obtained by multiplying number of sexual partners by number of occasions of unprotected sex; and (e) use of risk indexes that assign a number based on responses to general questions about risk behaviors. Data from 452 at‐risk women from a New England community were analyzed in 5 versions of an HIV/AIDS sexual risk prediction model. Models were compared in terms of SEM empirical fit indexes (x² [df], average absolute standardized residuals, and Comparative Fit Index); significant paths, explained variance, theoretical fit, and simplicity. Results indicate that: (a) multiple measures and latent factor models are preferable to all others by each of the standards of comparison, (b) in the composite dependent variable models, including information about the partners' number of partners provided little additional explained variance beyond knowing the number of occasions of unprotected sex, and (c) dependent measures that did not remain close to Centers for Disease Control criteria may not be adequately predicting HIV/AIDS sexual risk. Several recommendations are presented for selecting an appropriate conceptualization of HIV/AIDS sexual risk.     

Latent growth curves within developmental structural equation models

This report uses structural equation modeling to combine traditional ideas from repeated-measures ANOVA with some traditional ideas from longitudinal factor analysis. A longitudinal model that includes correlations, variances, and means is described as a latent growth curve model (LGM). When merged with repeated-measures data, this technique permits the estimation of parameters representing both individual and group dynamics. The statistical basis of this model allows hypothesis testing of various developmental ideas, including models of alternative dynamic functions and models of the sources of individual differences in these functions. Aspects of these latent growth models are illustrated with a set of longitudinal WISC data from young children and by using the LISREL V computer program.     

On desirability of parsimony in structural equation model selection

This article is concerned with issues in structural equation model selection that pertain to the general utility of the well‐known principle of parsimony. An example is provided using data generated by a relatively nonparsimonious simplex model and fitted rather well by a parsimonious growth curve model that belongs to a different class of models. Implications for empirical research are subsequently discussed, with emphasis on the extent to which one may be willing to routinely use parsimony as the only principle to follow in structural model selection.     

A structural equation modeling investigation of the emotional value of immersive virtual reality in education

Virtual reality (VR) is projected to play an important role in education by increasing student engagement and motivation. However, little is known about the impact and utility of immersive VR for administering e-learning tools, or the underlying mechanisms that impact learners’ emotional processes while learning. This paper explores whether differences exist with regard to using either immersive or desktop VR to administer a virtual science learning simulation. We also investigate how the level of immersion impacts perceived learning outcomes using structural equation modeling. The sample consisted of 104 university students (39 females). Significantly higher scores were obtained on 11 of the 13 variables investigated using the immersive VR version of the simulation, with the largest differences occurring with regard to presence and motivation. Furthermore, we identified a model with two general paths by which immersion in VR impacts perceived learning outcomes. Specifically, we discovered an affective path in which immersion predicted presence and positive emotions, and a cognitive path in which immersion fostered a positive cognitive value of the task in line with the control value theory of achievement emotions.     

领导班子的结构优化与心理互补

领导班子的结构优化是保证党的各项事业健康发展的基础 ,而班子成员的心理互补则是优化结构的重要因素。本文从如何优化领导班子结构出发 ,分析了成员心理结构调适协调的重要性和互补的三个原则。     

A review of using partial least square structural equation modeling in e-learning research

Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate statistical modeling technique that educational researchers frequently use. This paper reviews the uses of PLS-SEM in 16 major e-learning journals, and provides guidelines for improving the use of PLS-SEM as well as recommendations for future applications in e-learning research. A total of 53 articles using PLS-SEM published in January 2009–August 2019 are reviewed. We assess these published applications in terms of the following key criteria: reasons for using PLS-SEM, model characteristics, sample characteristics, model evaluations and reporting. Our results reveal that small sample size and nonnormal data are the first two major reasons for using PLS-SEM. Moreover, we have identified how to extend the applications of PLS-SEM in the e-learning research field.     

The equal correlation baseline model for comparative fit assessment in structural equation modeling

In this article I describe and evaluate an alternative baseline model for comparative fit assessment of structural equation models and compare it to the standard “null” baseline model. The new “equal correlation” baseline model constrains all variables to have equal, rather than zero, correlations, whereas all variances are free. The new baseline model reflects the reality of atheoretical background correlation in nonex‐perimental data sets, and it improves the ability of comparative fit indices to distinguish between better and worse target models. It also helps to preserve the statistical link between these indices and the noncentral χ² distribution. Also, computing the same comparative fit indices using different baseline models will provide more information about model fit than computing multiple comparative fit indices using the same baseline. I also point out some limitations of the proposed baseline model.     

基于结构方程模型对医学院校教师心理紧张反应的研究

影响心理紧张反应的因素有年龄、性别、学历、收入等个体特征,对支持利用度、时间控制、环境紧张因素、注意力需求、时间压力、合作需求、工作组织问题以及合作的可能性工作紧张因素,还有负性生活事件紧张因素都是影响教师心理紧张反应的主要因素。年龄越高,工龄越长、心理症状越明显,女性总体幸福感比男性低。工作紧张因素以及社会生活紧张因素都是影响其心理紧张反应的主要因素。     

Applying structural equation modeling in the context of the theory of reasoned action: Some problems and solutions

This study identifies and attempts to solve problems encountered in applications of structural equation modeling (SEM) to the theory of reasoned action. This theory is often used in social psychology and aims at explaining and predicting behavior. The few studies that test this theory with SEM have, in general, 2 methodological problems, which cast serious doubt on the validity of the conclusions. The first problem is that in most of the tests the data do not fit the model. The second problem is that part of the theory is formulated by multiplying 2 variables, which implies that the results are highly dependent on the arbitrarily chosen scale values. These problems are illustrated with a secondary analysis of survey data gathered by Burnkrant and Page (1988) and by new data presented in this study. In this article, an alternative model specification is proposed that strongly improves the fit of the data, but leaves intact the structural part of the model being tested. It is also advisable to omit 1 of the variables that forms part of the multiplicative composite.     

Examining technology acceptance of pre-service mathematics teachers in Turkey: A structural equation modeling approach

Education and Information Technologies - Among teacher beliefs, technology acceptance has a crucial role in effective technology integration into teaching. There is a need to examine the factors...     

成长性团体心理辅导对提高大学生自我效能感的作用

团体心理辅导由于其独特的运作机制,对提高大学生自信、改善人际关系有重要作用.自我效能感是个体成功达成目标的必要条件.通过实验设计证实,以"个人成长"为目标的成长性团体心理辅导以实践的方式,通过个人自身行为成败的经验、替代性经验、言语劝说、生理和情绪状态改变等途径,对提高大学生在团体中解决问题的自我效能感有重要作用.